Faculty Bibliography

BACKGROUND:Many kidneys are discarded every year, with 3631 kidneys discarded in 2016 alone. Identifying kidneys at high risk of discard could facilitate "rescue" allocation to centers more likely to transplant them. The Probability of Delay or Discard (PODD) model was developed to identify marginal kidneys at risk of discard or delayed allocation beyond 36 hours of cold ischemia time. However, PODD has not been prospectively validated, and patterns of discard may have changed after policy changes such as the introduction of Kidney Donor Profile Index and implementation of the Kidney Allocation System (KAS). METHODS:We prospectively validated the PODD model using Scientific Registry of Transplant Recipients data in the KAS era (January 1, 2015, to March 1, 2018). C statistic was calculated to assess accuracy in predicting kidney discard. We assessed clustering in centers' utilization of kidneys with PODD >0.6 ("high-PODD") using Gini coefficients. Using match run data from January 1, 2015, to December 31, 2016, we examined distribution of these high-PODD kidneys offered to centers that never accepted a high-PODD kidney. RESULTS:The PODD model predicted discard accurately under KAS (C-statistic, 0.87). Compared with utilization of low-PODD kidneys (Gini coefficient = 0.41), utilization of high-PODD kidneys was clustered more tightly among a few centers (Gini coefficient, 0.84 with >60% of centers never transplanted a high-PODD kidneys). In total, 11684 offers (35.0% of all high-PODD offers) were made to centers that never accepted a high-PODD kidney. CONCLUSIONS:Prioritizing allocation of high-PODD kidneys to centers that are more likely to transplant them might help reduce kidney discard.

Deceased donor kidney transplantation (DDKT) rates for highly sensitized (HS) candidates increased early after implementation of the Kidney Allocation System (KAS) in 2014. However, this may represent a bolus effect, and a granular investigation of the current state of DDKT for HS candidates remains lacking. We studied 270 722 DDKT candidates from the SRTR from 12/4/2011 to 12/3/2014 ("pre-KAS") and 12/4/2014 to 12/3/2017 ("post-KAS"), analyzing DDKT rates for HS candidates using adjusted negative binomial regression. Post-KAS, candidates with the highest levels of sensitization had an increased DDKT rate compared with pre-KAS (cPRA 98% adjusted incidence rate ratio [aIRR]:_1.27 1.77_2.46 P = .001, cPRA 99% aIRR:_3.18 4.36_5.98 P < .001, cPRA 99.5-99.9% aIRR:_16.91 24.29_34.89 P < .001, and cPRA 99.9%+ aIRR:_8.79 11.58_15.26 P < .001). To determine whether these changes produced more equitable access to DDKT, we compared DDKT rates of HS to non-HS candidates (cPRA 0-79%). Post-KAS, cPRA, 98% candidates had an equivalent DDKT rate (aIRR:_0.65 0.94_1.36 , P = .8) to non-HS candidates, whereas 99% candidates had a higher DDKT rate (aIRR:_1.19 1.68_2.38 , P = .02). Although cPRA 99.5-99.9% candidates had an increased DDKT rate (aIRR:_2.46 3.50_4.98 , P < .001) compared to non-HS candidates, cPRA 99.9%+ candidates had a significantly lower DDKT rate (aIRR:_0.29 0.40_0.56 , P < .001). KAS has improved access to DDKT for HS candidates, although substantial imbalance exists between cPRA 99.5-99.9% and 99.9%+ candidates.

Low T cell counts and acute rejection are associated with increased cardiovascular events (CVEs); T cell-depleting agents decrease both. Thus, we aimed to characterize the risk of CVEs by using an induction agent used in kidney transplant recipients. We conducted a secondary data analysis of patients who received a kidney transplant and used Medicare as their primary insurance from 1999 to 2010. Outcomes of interest were incident CVE, all-cause mortality, CVE-related mortality, and a composite outcome of mortality and CVE. Of 47 258 recipients, 29.3% received IL-2 receptor antagonist (IL-2RA), 33.3% received anti-thymocyte globulin (ATG), 7.3% received alemtuzumab, and 30.0% received no induction. Compared with IL-2RA, there was no difference in the risk of CVE in the ATG (adjusted hazard ratio [aHR] 0.98, 95% confidence interval [CI] 0.92-1.05) and alemtuzumab group (aHR 1.01, 95% CI 0.89-1.16), but slightly higher in the no induction group (aHR 1.06, 95% CI 1.00-1.14). Acute rejection did not modify this association in the latter group but did increase CVE by 46% in the alemtuzumab group. There was no difference in the hazard of all-cause or CVE-related mortality. Only in the ATG group, a 7% lower hazard of the composite outcome of mortality and CVE was noted. Induction agents are not associated with incident CVE, although prospective trials are needed to determine a personalized approach to prevention.

Delayed graft function (DGF) complicates 20%-40% of deceased-donor kidney transplants and is associated with increased length of stay and subsequent allograft failure. Accurate prediction of DGF risk for a particular allograft could influence organ allocation, patient counseling, and postoperative planning. Mitochondrial dysfunction, a reported surrogate of tissue health in ischemia-perfusion injury, might also be a surrogate for tissue health after organ transplantation. To understand the potential of mitochondrial membrane potential (MMP) in clinical decision-making, we analyzed whether lower MMP, a measure of mitochondrial dysfunction, was associated with DGF. In a prospective, single-center proof-of-concept study, we measured pretransplant MMP in 28 deceased donor kidneys and analyzed the association between MMP and DGF. We used hybrid registry-augmented regression to adjust for donor and recipient characteristics, minimizing overfitting by leveraging Scientific Registry of Transplant Recipients data. The range of MMP levels was 964-28 333 units. Low-MMP kidneys (MMP<4000) were more likely from female donors (75% vs 10%, P = .002) and donation after cardiac death donors (75% vs 12%, P = .004). For every 10% decrease in MMP levels, there were 38% higher odds of DGF (adjusted odds ratio = _1.08 1.38_1.78 , P = .01). In summary, MMP might be a promising pretransplant surrogate for tissue health in kidney transplantation and, after further validation, could improve clinical decision-making through its independent association with DGF.

The impact of donor quality on post-kidney transplant (KT) survival may vary by candidate condition. Characterizing this variation would increase access to KT without sacrificing outcomes. We developed a tool to estimate post-KT survival for combinations of donor quality and candidate condition. We studied deceased donor KT recipients (n = 120 818) and waitlisted candidates (n = 376 272) between 2005 and 2016 by using the Scientific Registry of Transplant Recipients. Donor quality and candidate condition were measured by using the Kidney Donor Profile Index (KDPI) and the Estimated Post Transplant Survival (EPTS) score. We estimated 5-year post-KT survival based on combinations of KDPI and EPTS score using random forest algorithms and waitlist survival by EPTS score using Weibull regressions. Survival benefit was defined as absolute reduction in mortality risk with KT. For candidates with an EPTS score of 80, 5-year waitlist survival was 47.6%, and 5-year post-KT survival was 78.9% after receiving kidneys with a KDPI of 20 and was 70.7% after receiving kidneys with a KDPI of 80. The impact of KDPI on survival benefit varied greatly by EPTS score. For candidates with low EPTS scores (eg, <40), the KDPI had limited impact on survival benefit. For candidates with middle or high EPTS scores (eg, >40), survival benefit decreased with higher KDPI but was still substantial even with a KDPI of 100 (>16 percentage points). Our prediction tool (www.transplantmodels.com/kdpi-epts) can support individualized decision-making on kidney offers in clinical practice.

Historically, exception points for hepatocellular carcinoma (HCC) led to higher transplant rates and lower waitlist mortality for HCC candidates compared to non-HCC candidates. As of October 2015, HCC candidates must wait 6 months after initial application to obtain exception points; the impact of this policy remains unstudied. Using 2013-2017 SRTR data, we identified 39  350 adult, first-time, active waitlist candidates and compared deceased donor liver transplant (DDLT) rates and waitlist mortality/dropout for HCC versus non-HCC candidates before (October 8, 2013-October 7, 2015, prepolicy) and after (October 8, 2015-October 7, 2017, postpolicy) the policy change using Cox and competing risks regression, respectively. Compared to non-HCC candidates with the same calculated MELD, HCC candidates had a 3.6-fold higher rate of DDLT prepolicy (aHR = _3.49 3.69 _3.89 ) and a 2.2-fold higher rate of DDLT postpolicy (aHR = _2.09 2.21 _2.34 ). Compared to non-HCC candidates with the same allocation priority, HCC candidates had a 37% lower risk of waitlist mortality/dropout prepolicy (asHR = _0.54 0.63 _0.73 ) and a comparable risk of mortality/dropout postpolicy (asHR = _0.81 0.95 _1.11 ). Following the policy change, the DDLT advantage for HCC candidates remained, albeit dramatically attenuated, without any substantial increase in waitlist mortality/dropout. In the context of sickest-first liver allocation, the revised policy seems to have established allocation equity for HCC and non-HCC candidates.

BACKGROUND:Every year, more than 5500 healthy people in the United States donate a kidney for the medical benefit of another person. The Organ Procurement and Transplantation Network (OPTN) requires transplant hospitals to monitor living kidney donors (LKDs) for 2 years postdonation. However, the majority (115/202, 57%) of transplant hospitals in the United States continue to fail to meet nationally mandated requirements for LKD follow-up. A novel method for collecting LKD follow-up is needed to ease both the transplant hospital-level and patient-level burden. We built mKidney-a mobile health (mHealth) system designed specifically to facilitate the collection and reporting of OPTN-required LKD follow-up data. The mKidney mobile app was developed on the basis of input elicited from LKDs, transplant providers, and thought leaders. OBJECTIVE:The primary objective of this study is to evaluate the impact of the mKidney smartphone app on LKD follow-up rates. METHODS:We will conduct a two-arm randomized controlled trial (RCT) with LKDs who undergo LKD transplantation at Methodist Specialty and Transplant Hospital in San Antonio, Texas. Eligible participants will be recruited in-person by a study team member at their 1-week postdonation clinical visit and randomly assigned to the intervention or control arm (1:1). Participants in the intervention arm will receive the mHealth intervention (mKidney), and participants in the control arm will receive the current standard of follow-up care. Our primary outcome will be policy-defined complete (all components addressed) and timely (60 days before or after the expected visit date) submission of LKD follow-up data at required 6-month, 1-year, and 2-year visits. Our secondary outcome will be hospital-level compliance with OPTN reporting requirements at each visit. Data analysis will follow the intention-to-treat principle. Additionally, we will collect quantitative and qualitative process data regarding the implementation of the mKidney system. RESULTS:We began recruitment for this RCT in May 2018. We plan to enroll 400 LKDs over 2 years and follow participants for the 2-year mandated follow-up period. CONCLUSIONS:This pilot RCT will evaluate the impact of the mKidney system on rates of LKD and hospital compliance with OPTN-mandated LKD follow-up at a large LKD transplant hospital. It will provide valuable information on strategies for implementing such a system in a clinical setting and inform effect sizes for future RCT sample size calculations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/11000.

The increased use of split-liver transplantation (SLT) represents a strategy to increase the supply of organs. Although outcomes after SLT and whole liver transplantation (WLT) are similar on average among pediatric recipients, we hypothesized that the relationship between graft type and outcomes may vary depending on patient, donor, and surgical characteristics. We evaluated graft survival among pediatric (<18 years) deceased donor, liver-only transplant recipients from March 2002 until December 2015 using data from the Scientific Registry of Transplant Recipients. Graft survival was assessed in a Cox proportional hazards model, with and without effect modification between graft type and donor, recipient, and surgical characteristics, to identify conditions where the risk of graft loss for SLT and WLT were similar. In a traditional multivariable model, characteristics associated with graft loss included donor age >50 years, recipient weight <10 kg, acute hepatic necrosis, autoimmune diseases, tumor, public insurance, and cold ischemia time (CIT) >8 hours. In an analysis that explored whether these characteristics modified the relationship between graft type and graft loss, many characteristics associated with loss actually had similar outcomes regardless of graft type, including weight <10 kg, acute hepatic necrosis, autoimmune diseases, and tumor. In contrast, several subgroups had worse outcomes when SLT was used, including recipient weight 10-35 kg, non-biliary atresia cholestasis, and metabolic disease. Allocation score, share type, or CIT did not modify risk of graft type and graft failure. Although one might anticipate that individuals with higher rates of graft loss would be worse candidates for SLT, data suggest that these patients actually have similar rates of graft loss. These findings can guide surgical decision making and may support policy changes that promote the increased use of SLT for specific pediatric recipients.

A recent study reported that kidney transplant recipients of offspring living donors had higher graft loss and mortality. This seemed counterintuitive, given the excellent HLA matching and younger age of offspring donors; we were concerned about residual confounding and other study design issues. We used Scientific Registry of Transplant Recipients data 2001-2016 to evaluate death-censored graft failure (DCGF) and mortality for recipients of offspring versus nonoffspring living donor kidneys, using Cox regression models with interaction terms. Recipients of offspring kidneys had lower DCGF than recipients of nonoffspring kidneys (15-year cumulative incidence 21.2% vs 26.1%, P < .001). This association remained after adjustment for recipient and transplant factors (adjusted hazard ratio [aHR] = _0.73 0.77_0.82 , P < .001), and was attenuated among African American donors (aHR _0.77 0.85_0.95 ; interaction: P = .01) and female recipients (aHR _0.77 0.84_0.91 , P < .001). Although offspring kidney recipients had higher mortality (15-year mortality 56.4% vs 37.2%, P < .001), this largely disappeared with adjustment for recipient age alone (aHR = _1.02 1.06_1.10 , P = .002) and was nonsignificant after further adjustment for other recipient characteristics (aHR = _0.93 0.97_1.01 , P = .1). Kidneys from offspring donors provided lower graft failure and comparable mortality. An otherwise eligible donor should not be dismissed because they are the offspring of the recipient, and we encourage continued individualized counseling for potential donors.